Built on

J. W. Forrester, “Counterintuitive behavior of social systems,” Theory and decision , vol. 2, no. 2, pp. 109–140, 1971
1971
Earlier work this paper cites.
G. B. Keller, T. Bonhoeffer, and M. Hübener, “Sensorimotor mismatch signals in primary visual cortex of the behaving mouse,” Neuron , vol. 74, no. 5, pp. 809–815, 2012
2012
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G. W. Maus, J. Fischer, and D. Whitney, “Motion-dependent representation of space in area mt+,” Neuron , vol. 78, no. 3, pp. 554–562, 2013
2013
Earlier work this paper cites.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555 , 2014
Original
2014
Earlier work this paper cites.
N. Nortmann, S. Rekauzke, S. Onat, P. König, and D. Jancke, “Primary visual cortex represents the difference between past and present,” Cerebral Cortex , vol. 25, no. 6, pp. 1427–1440, 2015
2015
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M. Leinweber, D. R. Ward, J. M. Sobczak, A. Attinger, and G. B. Keller, “A sensorimotor circuit in mouse cortex for visual flow predictions,” Neuron , vol. 95, no. 6, pp. 1420–1432, 2017
2017
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L. Pinto, M. Andrychowicz, P. Welinder, W. Zaremba, and P. Abbeel, “Asymmetric actor critic for image-based robot learning,” arXiv preprint arXiv:1710.06542 , 2017
Original
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017
Original
2017
Earlier work this paper cites.
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots,” arXiv preprint arXiv:1804.10332 , 2018
Original
2018
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M. Mohri, “Foundations of machine learning,” 2018
2018
Earlier work this paper cites.
D. Ha and J. Schmidhuber, “World models,” arXiv preprint arXiv:1803.10122 , 2018
Original
2018
Earlier work this paper cites.
K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” in Advances in Neural Information Processing Systems , 2018, pp. 4754–4765
2018
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Y. Luo, H. Xu, Y. Li, Y. Tian, T. Darrell, and T. Ma, “Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees,” arXiv preprint arXiv:1807.03858 , 2018
Original
2018
Earlier work this paper cites.
W. Yu, V. C. Kumar, G. Turk, and C. K. Liu, “Sim-to-real transfer for biped locomotion,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 3503–3510
2019
Earlier work this paper cites.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
2019
Earlier work this paper cites.
A. Anand, E. Racah, S. Ozair, Y. Bengio, M.-A. Côté, and R. D. Hjelm, “Unsupervised state representation learning in atari,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.

Similar

D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” arXiv preprint arXiv:1912.01603 , 2019
Original
2019
Cited alongside, same era.
M. Janner, J. Fu, M. Zhang, and S. Levine, “When to trust your model: Model-based policy optimization,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” in International conference on machine learning . PMLR, 2019, pp. 2555–2565
2019
Cited alongside, same era.
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” arXiv preprint arXiv:2004.00784 , 2020
Original
2020
Cited alongside, same era.

Then

I. M. A. Nahrendra, B. Yu, and H. Myung, “Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5078–5084
2023
Later among the works it cites.
J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in Conference on robot learning . PMLR, 2023, pp. 403–415
2023
Later among the works it cites.
Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao, “Robot parkour learning,” arXiv preprint arXiv:2309.05665 , 2023
Original
2023
Later among the works it cites.

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2024

World Model-based Perception for Visual Legged Locomotion

Lai, Hang, Cao, Jiahang, Xu, Jiafeng et al.

Understand

Legged locomotion over various terrains is challenging and requires precise perception of the robot and its surroundings from both proprioception and vision.

However, learning directly from high-dimensional visual input is often data-inefficient and intricate.
To address this issue, traditional methods attempt to learn a teacher policy with access to privileged information first and then learn a student policy to imitate the teacher's behavior with visual input.
Despite some progress, this imitation framework prevents the student policy from achieving optimal performance due to the information gap between inputs.

Built on

J. W. Forrester, “Counterintuitive behavior of social systems,” Theory and decision , vol. 2, no. 2, pp. 109–140, 1971
1971
Earlier work this paper cites.
G. B. Keller, T. Bonhoeffer, and M. Hübener, “Sensorimotor mismatch signals in primary visual cortex of the behaving mouse,” Neuron , vol. 74, no. 5, pp. 809–815, 2012
2012
Earlier work this paper cites.
G. W. Maus, J. Fischer, and D. Whitney, “Motion-dependent representation of space in area mt+,” Neuron , vol. 78, no. 3, pp. 554–562, 2013
2013
Earlier work this paper cites.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555 , 2014
Original
2014
Earlier work this paper cites.
N. Nortmann, S. Rekauzke, S. Onat, P. König, and D. Jancke, “Primary visual cortex represents the difference between past and present,” Cerebral Cortex , vol. 25, no. 6, pp. 1427–1440, 2015
2015
Earlier work this paper cites.
M. Leinweber, D. R. Ward, J. M. Sobczak, A. Attinger, and G. B. Keller, “A sensorimotor circuit in mouse cortex for visual flow predictions,” Neuron , vol. 95, no. 6, pp. 1420–1432, 2017
2017
Earlier work this paper cites.
L. Pinto, M. Andrychowicz, P. Welinder, W. Zaremba, and P. Abbeel, “Asymmetric actor critic for image-based robot learning,” arXiv preprint arXiv:1710.06542 , 2017
Original
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017
Original
2017
Earlier work this paper cites.
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots,” arXiv preprint arXiv:1804.10332 , 2018
Original
2018
Earlier work this paper cites.
M. Mohri, “Foundations of machine learning,” 2018
2018
Earlier work this paper cites.
D. Ha and J. Schmidhuber, “World models,” arXiv preprint arXiv:1803.10122 , 2018
Original
2018
Earlier work this paper cites.
K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” in Advances in Neural Information Processing Systems , 2018, pp. 4754–4765
2018
Earlier work this paper cites.
Y. Luo, H. Xu, Y. Li, Y. Tian, T. Darrell, and T. Ma, “Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees,” arXiv preprint arXiv:1807.03858 , 2018
Original
2018
Earlier work this paper cites.
W. Yu, V. C. Kumar, G. Turk, and C. K. Liu, “Sim-to-real transfer for biped locomotion,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 3503–3510
2019
Earlier work this paper cites.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
2019
Earlier work this paper cites.
A. Anand, E. Racah, S. Ozair, Y. Bengio, M.-A. Côté, and R. D. Hjelm, “Unsupervised state representation learning in atari,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.

Similar

D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” arXiv preprint arXiv:1912.01603 , 2019
Original
2019
Cited alongside, same era.
M. Janner, J. Fu, M. Zhang, and S. Levine, “When to trust your model: Model-based policy optimization,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” in International conference on machine learning . PMLR, 2019, pp. 2555–2565
2019
Cited alongside, same era.
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” arXiv preprint arXiv:2004.00784 , 2020
Original
2020
Cited alongside, same era.

Then

I. M. A. Nahrendra, B. Yu, and H. Myung, “Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5078–5084
2023
Later among the works it cites.
J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in Conference on robot learning . PMLR, 2023, pp. 403–415
2023
Later among the works it cites.
Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao, “Robot parkour learning,” arXiv preprint arXiv:2309.05665 , 2023
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, p. eabc5986, 2020

2020

Cited alongside, same era.

D. Hafner, T. Lillicrap, M. Norouzi, and J. Ba, “Mastering atari with discrete world models,” arXiv preprint arXiv:2010.02193 , 2020

Original

2020

Cited alongside, same era.

A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” arXiv preprint arXiv:2107.04034 , 2021

Original

2021

Cited alongside, same era.

Z. Fu, A. Kumar, J. Malik, and D. Pathak, “Minimizing energy consumption leads to the emergence of gaits in legged robots,” arXiv preprint arXiv:2111.01674 , 2021

Original

2021

Cited alongside, same era.

Z. Xie, X. Da, M. van de Panne, B. Babich, and A. Garg, “Dynamics randomization revisited: A case study for quadrupedal locomotion,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4955–4961

2021

Cited alongside, same era.

V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa et al. , “Isaac gym: High performance gpu-based physics simulation for robot learning,” arXiv preprint arXiv:2108.10470 , 2021

Original

2021

Cited alongside, same era.

T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022

2022

Cited alongside, same era.

N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning . PMLR, 2022, pp. 91–100

2022

Cited alongside, same era.

H. He, C. Bai, H. Lai, L. Wang, and W. Zhang, “Privileged knowledge distillation for sim-to-real policy generalization,” arXiv preprint arXiv:2305.18464 , 2023

Original

2023